Identification of differentially expressed snoRNAs in Ovarian Cancer from RNA-Seq data
Bibliographic record
Abstract
Non-coding RNAs (ncRNAs) are a large class of important regulatory molecules involved in many physiological and cellular processes. Small nucleolar RNAs (snoRNAs), a subset of the small ncRNAs, are molecules located in the cell nucleolus. Even though, they are one of the most ancient and evolutionary conserved ncRNAs, they are gaining more prominence and attention in the recent years only. The classical function of snoRNAs is to act as guide RNAs of rRNAs and nucleolytic processing of the rRNA transcripts. However several scientific evidences have indicated that other than the classical functions, they are involved in multiple functions such as metabolic stress regulation, modulation of alternative splicing, controlling cell behavior, etc. and the dysregulation of snoRNAs could contribute to carcinogenesis. Even though many independent works have been carried out, to examine the role of snoRNAs in several human diseases including cancer, specific projects to study the cumulative role of snoRNAs in a disease is limited. The advent of high throughput and deep sequencing technologies has opened up new avenues for carrying out such studies. This study focusses on the utilization of snoRNAs as potential biomarkers characteristic to ovarian cancer based on a RNA-Seq data. We have downloaded a transcriptome data, [PRJNA209481] from the NCBI BioProject, pertaining to human ovarian cancer cell lines. Downstream analysis was done by Tuxedo pipeline of RNA-Seq data analysis. The differential expression analysis by Cuffdiff threw a total of 847 differentially expressed genes, of which we found, 71 snoRNAs to be up regulated and 34 snoRNAs as down regulated. Many of the previously reported snoRNAs having a role in tumorigenesis in a variety of cancers are also seen to be dysregulated in this study. Further investigation is underway to analyze the effect of differentially expressed snoRNAs in ovarian cancer and their potential as biomarkers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".